Summary:
Right now we don't insert additional observers (share observers) if qspec.dtype and qspec.is_dynamic matches exactly,
since fixed qparams quantization spec and derived quantization spec do have have is_dynamic field curerntly, observer sharing does not happen between them and quantization spec, in this PR we fixed the issue by
adding is_dynamic to all quantization specs.
Note: SharedQuantizationSpec should probably be its own type in the future
TODO later:
(1). move all these fields (dtype, is_dynamic, quant_min, quant_max etc.) to QuantizationSpecBase,
(2). make SharedQuantizationSpec a separate type
(3). add quant_min/quant_max in observer sharing checking in pt2e/prepare.py
Test Plan:
python test/test_quantization.py -k test_fixed_qparams_qspec_observer_dedup
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D55396546](https://our.internmc.facebook.com/intern/diff/D55396546)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122734
Approved by: https://github.com/andrewor14
Summary:
For a Node: node1 and edge: (node1, node2), since they are observing the same
Tensor, we may want to implicitly share observers, this flag allows people to
turn off this behavior for the output of the node
See the test_allow_implicit_sharing test for use case
Test Plan:
python test/test_quantization.py TestQuantizePT2E.test_allow_implicit_sharing
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112929
Approved by: https://github.com/kimishpatel
Summary: att, after SharedQuantizationSpec bug fix we are doing some checks before hand, this can simplify the logic when we insert observers
Test Plan:
contbuild & OSS CI, see bf998a2c5d
Test plan from GitHub:
python test/test_quantization.py TestQuantizePT2E
CIs
Differential Revision: D50816224
Pulled By: jerryzh168
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112453
Approved by: https://github.com/andrewor14
Summary:
att, after SharedQuantizationSpec bug fix we are doing some checks before hand, this can simplify the logic when we insert observers
Test Plan:
python test/test_quantization.py TestQuantizePT2E
CIs
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111828
Approved by: https://github.com/kimishpatel
ghstack dependencies: #111827
Summary:
Previously we can only use native pytorch int dtypes that has corresponding quantized dtypes (e.g. quint8, qint8), this
PR removes this assumption in observers/fake_quants so that users can use all pytorch native dtypes (except for int64, we can add it later if need)
the main addition here is int16.
Test Plan:
python test/test_quantization.py TestQuantizePT2E
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108453
Approved by: https://github.com/kimishpatel
Summary:
Currently in quantizer/quantize_pt2e we import things from specific quantizers (XNNPACKQuantizer, QuantizationConfig) etc.
this PR removes them so it's clearer that they are not part of the core quantization code base
This PR also removed get_supported_operators from main Quantizer since we haven't seen a clear need for this API
Test Plan:
CIs
Imported from OSS
Differential Revision: D48340367
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107259
Approved by: https://github.com/kimishpatel
Summary: moving quantizer to torch.ao.quantization to make it a public api, since pt2e is a folder for implementations
Test Plan:
CIs
sanity check: "buck test //executorch/backends/xnnpack/test:test_xnnpack_quantized_models -- test_resnet18"
Differential Revision: D47727838
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105885
Approved by: https://github.com/andrewor14
